Global and regional models have large systematic errors in their modelled dust fields over West Africa. It is well established that cold-pool outflows from moist convection (haboobs) can raise over 50 % of the dust over parts of the Sahara and Sahel in summer, but parameterised moist convection tends to give a very poor representation of this in models. Here, we test the hypothesis that an explicit representation of convection in the Met Office Unified Model (UM) improves haboob winds and so may reduce errors in modelled dust fields. The results show that despite varying both grid spacing and the representation of convection there are only minor changes in dust aerosol optical depth (AOD) and dust mass loading fields between simulations. In all simulations there is an AOD deficit over the observed central Saharan dust maximum and a high bias in AOD along the west coast: both features are consistent with many climate (CMIP5) models. Cold-pool outflows are present in the explicit simulations and do raise dust. Consistent with this, there is an improved diurnal cycle in dust-generating winds with a seasonal peak in evening winds at locations with moist convection that is absent in simulations with parameterised convection. However, the explicit convection does not change the AOD field in the UM significantly for several reasons. Firstly, the increased windiness in the evening from haboobs is approximately balanced by a reduction in morning winds associated with the breakdown of the nocturnal low-level jet (LLJ). Secondly, although explicit convection increases the frequency of the strongest winds, they are still weaker than observed, especially close to the observed summertime Saharan dust maximum: this results from the fact that, although large mesoscale convective systems (and resultant cold pools) are generated, they have a lower frequency than observed and haboob winds are too weak. Finally, major impacts of the haboobs on winds occur over the Sahel, where, although dust uplift is known to occur in reality, uplift in the simulations is limited by a seasonally constant bare-soil fraction in the model, together with soil moisture and clay fractions which are too restrictive of dust emission in seasonally varying vegetated regions. For future studies, the results demonstrate (1) the improvements in behaviour produced by the explicit representation of convection, (2) the value of simultaneously evaluating both dust and winds and (3) the need to develop parameterisations of the land surface alongside those of dust-generating winds.
During the summer season the Sahara is the world's largest source of mineral
dust (Ginoux et al., 2012; Prospero et al., 2002) and representations of dust
are known to improve numerical weather prediction (NWP) models (Haywood et
al., 2005; Tompkins et al., 2005; Rodwell and Jung, 2008), although the accuracy of dust
forecasts remains limited (Chaboureau et al., 2016; Huneeus et al.,
2016; Terradellas et al., 2016). Dust is also a prognostic variable in
several climate models, although its value has been questioned due to the
poor performance of the models in representing dust variability (Evan et al.,
2014). There is, therefore, a need to improve dust models across timescales
and a need to improve the representation of both the land surface that emits
dust and dust-generating winds. For winds it is known that rare, high wind
speed events are disproportionately important for raising dust (Cowie
et al., 2015) and that a poor representation of cold-pool outflows from moist
convection (haboobs: Roberts and Knippertz, 2012) is one major limitation of
summertime winds in current models for the Sahara and Sahel (Marsham et al.,
2011; Knippertz and Todd, 2010). Haboobs can range in size from tens to
hundreds of kilometres across and rare,
large events can be some of the largest single uplift
events in West Africa (here defined as the United Nations subregion of West Africa and Algeria, Morocco, Tunisia and Western Sahara; Roberts and Knippertz, 2014). Although often considered
a Sahelian phenomenon (in West Africa), haboobs were shown by Marsham et
al. (2013) and Allen et al. (2013) to be observed commonly at Bordj Badji
Mokhtar in the central Sahara (21.38
Several meteorological processes are known to raise mineral dust. Synoptic-scale systems (Johnson and Osborne, 2011) and the breakdown of nocturnal low-level jets (Knippertz, 2008; Fiedler et al., 2013) are of sufficiently large scale to be captured by many models (Woodage et al., 2010; Johnson et al., 2011). However, it is estimated that dust raised by convectively generated cold-pool outflows contribute over 50 % of the summertime uplift in some areas of the Sahel and Sahara (Marsham et al., 2013; Allen et al., 2013, 2014; Heinold et al., 2013) and may explain the seasonal cycle of dust in the region (Marsham et al., 2008). The parameterised representation of convection in global models can make haboobs essentially non-existent (Marsham et al., 2011) and, consistent with this, data assimilation has shown that an NWP model with prognostic dust underestimates dust in regions of observed haboobs (Pope et al., 2016). The comparison of observed near-surface winds with meteorological reanalyses in key dust uplift areas (Largeron et al., 2015; Roberts et al., 2017) highlights that even such analyses, which are constrained by assimilation of available observations (and often used as de-facto observations), have large systematic biases. In particular, the distribution of wind speed in analyses misses the high wind speed tail, the seasonal and diurnal cycles have amplitudes that are too small and the seasonal evening peak in winds associated with cold pools is missing. A common feature of many previously conducted evaluations of models or analyses is that they evaluate only the dust (usually AOD, e.g. Johnson, 2011; Párez et al., 2011) or the winds (e.g. Largeron et al., 2015; Roberts et al., 2017) and not both the dust emission and surface winds. This is despite it being known that there are likely to be systematic biases in both model winds and dust. Without an investigation of the winds alongside the dust it is impossible to judge whether a successful replication of dust fields are a result of compensating errors or whether all process involved (including transport and deposition) are correctly represented.
Recent modelling work has attempted to address the role of haboobs in models by resolving convection explicitly with high-resolution simulations (Cascade; Birch et al., 2014; Pearson et al, 2014) and applying an offline dust model (Heinold et al., 2013); this highlighted the importance of convective cold pools as well as the representation of near-surface night-time stability. Despite the improved diurnal cycle in windiness associated with cold pools using this approach, it is important to recognise that simulations capable of producing organised convective storms are not automatically able to represent near-surface winds of cold pools. Simulated cold pools are likely to differ from real-world examples in terms of size, duration and wind speed. Another approach has been the development and application of a haboob parameterisation, in which additional low-level winds are added that are linked to mass fluxes from the convection scheme (Pantillon et al., 2015, 2016). This approach led to an improved agreement between the potential dust uplift in convection-permitting simulations and those with parameterised convection. However, this method obviously does not seek to correct the diurnal cycle bias in rainfall (where peak rain occurs close to midday in parameterised convection simulations, and in the evening in convection permitting simulations and in reality) or evaluate winds from convection-permitting simulations against observations in any detail. Chaboureau et al. (2016) compared near-surface winds and prognostic dust from in-line simulations with both explicit and parameterised convection. They show some success in increasing the occurrence of strong winds in the evening (haboobs) when explicitly representing convection, and in improving the dust AOD biases relative to observations by increasing AOD values in the southern Sahara and northern Sahel. They also show improvements to the meridional AOD gradient to the west of the Sahara. However, the variability in AOD at specific sites, including very high values associated with convectively active African easterly waves, is still underrepresented even with explicit convection. In Chaboureau et al. (2016) simulations were re-initialised daily, preventing the modification of the large-scale monsoon flow by convective storms (Marsham et al., 2011, 2013; Garcia Carreras et al., 2013). They also encompassed only part of the summer season (25 July–2 September 2006; Heinold et al., 2013; 1 June–30 July 2006; Pantillon et al., 2015 and 1–30 June 2011; Chaboureau et al., 2016) so do not show the full seasonal evolution and were not able to clearly demonstrate the impact of resolved versus parameterised convection in models that were otherwise identical.
The Saharan – West African Monsoon Multi-scale Analysis (SWAMMA) project simulations used in this study have a range of horizontal grid spacing (4–40 km) and have both convection-permitting and parameterised convection set-ups. They are performed over a full summer season (1 May–30 September 2011) with a fully interactive mineral dust scheme. Although lateral boundary conditions are updated hourly, the size of the domain and duration of the runs means that away from boundaries model fields can diverge from the parent model, allowing the evolution of the hydrological and dust cycles in each simulation. The authors believe this to be the first reported study of large domain multi-day convection-permitting simulations with prognostic dust over the Sahara and Sahel. The approach of using both dust AOD retrievals and observations of near-surface wind speed to evaluate simulations also makes this work novel and gives an unprecedented opportunity to attribute errors in dust uplift as well as in AOD magnitude and distribution. The arrangement of this paper is as follows: Sect. 2 describes the model set-up, experiments performed and observations used to validate the model. Results are presented in Sect. 3, in which model dust AODs, emissions, low-level winds and storm development are compared between the different models and with observations. Discussion of the results and conclusions follow in Sect. 4.
SWAMMA simulations use a limited-area version of the UK Met Office (UKMO)
Unified Model (UM), based on the HadGEM3-RA regional climate model
previously tested at various resolutions over Africa (Moufouma-Okia and
Jones, 2015). The UM is designed to function across a wide range of spatial
and temporal scales and is used for meteorology and climate research as well
as operational numerical weather prediction. The UM (version 8.2 is used
here) consists of a dynamical core (Davies et al., 2005; Staniforth et al.,
2006) which describes evolution of the atmosphere as a non-hydrostatic,
fully compressible fluid. Model levels are terrain which is close to the
surface but relaxes to smooth, parallel levels at height. The model has a
fixed Eulerian grid but utilises the semi-implicit, semi-Lagrangian
time stepping to advect variables (allowing for mass conservation). Physics
packages include a two-stream radiation code (Edwards et al., 2012), the Joint
UK Land Environment Simulator (JULES) land surface exchange scheme (Best, 2005; Best et al., 2011),
boundary layer turbulence (Lock and Edwards, 2012),
cloud microphysics (Wilkinson, 2012) and convection (Stratton et al., 2009).
The SWAMMA simulations use a limited area set-up with a domain encompassing
all of West Africa (approximately 0–35
Maps of
Another important improvement on the Cascade simulations is the inclusion of
prognostic interactive dust in the SWAMMA simulations. This allows for the
investigation of the dust-raising and transportation characteristics of the
model under varying resolutions and convection options, as well as assessing
the radiative impact that dust has on the WAM system. The dust scheme used
is that within the Coupled Large-scale Aerosol Simulator for Studies in
Climate (CLASSIC; Johnson et al., 2011) scheme, in which dust particles are
assumed to be spherical and transported in the atmosphere as six independent
tracers undergoing dry deposition through turbulent mixing and gravitational
settling as well as wet deposition through washout from precipitation. Dust
emissions are calculated during each model time step using prognostic model
fields. The dust emission scheme utilises the widely used algorithm of
Marticorena and Bergametti (1995) to calculate horizontal flux in each of
nine bins with boundaries at 0.0316, 0.1, 0.316, 1.0, 3.16, 10.0, 31.6, 100., 316
and 1000
Summary of model simulations run in SWAMMA.
Within the framework described above, eight simulations are conducted which comprise the SWAMMA model suite. The main variable factors between the simulations are grid spacing, representation of convection and radiatively interactive mineral dust (see Table 1). In simulations with parameterised convection the convective scheme in the UM is switched on (Stratton et al., 2009). This scheme is based on a convective available potential energy (CAPE) closure method, where high CAPE values are identified and tendencies are determined to reduce this over a given timescale. In the simulations with explicit convection the convective parameterisation has effectively been switched off by increasing the CAPE closure timescale to a point at which CAPE depletion by the parameterisation is insignificant. These models employ a Smagorinsky-style subgrid-scale mixing in all three dimensions (3DS in Table 1) with mixing length constants chosen as those found optimal for the 12 and 4 km models in Cascade (0.05 and 0.1 respectively). In the simulations with radiatively active dust, mineral dust emitted from the surface within the simulations influences the radiation budget via its direct radiative effect (scattering and absorbing solar and thermal radiation); cloud microphysical effects are not included. While dust is present in the radiatively inactive simulations it does not influence the radiation budget or the evolution of the model meteorology. Comparing simulations with different convection types but without dust effects (e.g. 12P and 12E in Table 1) highlights the impact of resolved convection on dust generation without complications of feedbacks through dust–radiation interactions. We focus on the latter in this paper, although here we note that effects of interactive dust on both dust uplift itself and thermodynamics are far smaller than those that change the convection (not shown).
We use AOD at 550 nm from the Moderate Resolution Imaging Spectroradiometer
(MODIS) Collection 6 merged scientific data set (SDS) available from the NASA
Giovanni online data system (Acker and Leptoukh, 2007). This data set
combines the new enhanced deep blue (DB) SDS, now available over all
cloud-free and snow-free land surfaces (and therefore including dark
vegetated surfaces), and dark target (DT) land and ocean SDS (Sayer et
al., 2014). This produces a more spatially complete SDS over both land and
ocean. The DB algorithm has provided a much improved technique for the
retrieval of AOD values over bright surfaces compared to DT. Maps and
libraries of surface reflectance in the blue part of the spectrum are used
to produce AOD values that compare well with the AErosol RObotic NEtwork
(AERONET). The estimated error is 0.05
It is also noteworthy that most of the available in situ observations
(AERONET and the AMMA dust transect
Monthly mean (May–September, left-right) aerosol optical depths (AODs) at
10:00 UTC from
We note that there are anomalously high MODIS-merged AODs present in Fig. 2
in June around 0–10
False colour red–green–blue (RGB) dust imagery from the EUMETSAT Spinning
Enhanced Visual and Infrared Imager (SEVIRI) is used to give a qualitative
understanding of the uplift of dust associated with a large cold pool. The
15 min time resolution and very wide field of view mean SEVIRI data are
extremely useful for visual tracking and interpreting the development of
individual systems. To highlight regions of raised dust the product compares
brightness temperature and brightness temperature differences between three
of SEVIRI's infrared channels (channels 7, 9 and 10 which correspond to 8.7,
10.8 and 12
The AERUS-GEO (Aerosol and surface albEdo Retrieval Using a directional
Splitting method-application to GEOstationary data) AOD is a daily daytime-only
mean measure of AOD (Carrer et al., 2014). The approach used to produce the
AERUS-GEO product is detailed in Carrer et al. (2010) and Carrer et al. (2014). The relatively invariant nature of the land surface albedo on a
daily timescale compared to the atmosphere is used along with the high
temporal resolution of SEVIRI retrievals (full disc scan every 15 min)
to distinguish the 0.63
Wind speed observations from several in situ observation platforms are compared
with simulated wind speeds. Data from five stations are used, these
are Fennec automatic weather stations (AWSs) 134 (23.5
The Fennec project aimed to improve the understanding of Saharan meteorology with a particular focus on the processes associated with dust uplift and transport. Eight Fennec AWSs were distributed across the Sahara in Algeria and Mauritania in late May 2011 and continued to operate into 2013. The structure of the AWSs and the observations that were made are detailed in Hobby et al. (2013). Unfortunately during 2011 a number of the AWSs experienced problems associated with overheating, leaving only F-134 and F-138 with good data coverage over the SWAMMA simulation period (Roberts et al., 2017). Wind observations were transmitted via satellite and comprised 3 min 20 s mean wind speed values from the cup anemometers at 2 m a.g.l.
Also deployed as part of the Fennec campaign was a more comprehensive suite of instruments at two supersites at BBM (Algeria) and Zourate (Mauritania). The wind speed observations that are used in this study are from the flux tower deployed at BBM (Zourate data do not extend sufficiently over the simulated period). The supersite has no wind speed data for May but has data for 25 days in June, 31 days in July, 31 days in August and 3 days in September. This allows for comparison between simulations and observations for 3 of the 5 simulated months within the West African summertime dust hotspot (Englestaedter and Washington, 2007; Knippertz and Todd, 2010). Marsham et al. (2013) detail the instrumentation deployed at the BBM supersite. Wind measurements used in this study are from a sonic anemometer positioned at 10 m a.g.l. The sampling frequency is 20 Hz but 1 h means have been calculated for comparison with simulations and other observed winds.
The AMMA field campaign (Lebel et al., 2011), primarily conducted in 2006, had the aim of improving the understanding of the WAM system. Observations over a large area and over a large timescale were conducted, including the deployment of AWSs. Of the many AWSs deployed, two of those have been used in this study and were part of the AMMA CATCH programme, which specifically had the objective of looking at interannual variability of the WAM system. These stations (Agoufou and Kobou), were deployed ready for the main AMMA-observing period in 2006 and were still operational in 2011. This allows for unprecedented comparison between simulations and observations in the Sahel and Sahara, with observations that are temporally coincident.
To investigate the nature of mesoscale convective systems seen in
observations and those generated in convection-permitting simulations a
storm-tracking approach has been adopted. The algorithm used is based on
that of Stein et al. (2014) and has been modified for use on both
simulations and observations (Crook et al., 2018). The algorithm
can be applied to either rainfall or brightness temperatures to track
convective systems over West Africa. Storm clusters are identified through
the use of a threshold and by grouping contiguous cells. These are then
followed in time using a fractional overlap method (0.6 overlap threshold)
to track storm cells, allowing for both cell splitting and merging. If a
storm has no overlapping cells from the previous time step, then it is a new
initiation. When a storm has no overlapping cells in the next time step, it
is a dissipation. For splits the cell with the greatest overlap retains its
storm ID, while other cells are said to have split and are given new storm
IDs (parent IDs are recorded). Similarly, for merging, the cell from the
previous time step with the greatest overlap with the resultant cluster
maintains its ID and any other cells with smaller overlaps are said to have
merged and take the ID of the cell with the largest overlap. For this study
it was decided that a brightness temperature approach, using a threshold of
Comparisons of dust AODs with observations are frequently used to verify (and
in many cases, tune) dust models (Huneeus et al., 2011, 2016). This is because
AOD observations from satellites are now available at
high temporal and spatial resolutions, unlike observations of dust emissions
and concentrations. However, within a modelling framework AOD is very much an
end product, requiring not only accurate representations of all the physical
processes involved in dust emission, transport and deposition to achieve
realistic dust loadings but also accurate representations of particle size
distribution and spectral optical properties. For example, in the SWAMMA
experiments, although extinction per unit mass is greatest for particle size
division 2 (0.1–0.3
The dust loadings in the SWAMMA experiments (5–6 Tg May to September seasonal
mean for the whole domain) are at the low end of, but not outside, the range
reported by other modelling studies (this of course could be resolved by
tuning total emissions, but would not affect the systematic model biases we
investigate here); Huneeus et al. (2011) reviewed 15 global models
within the AeroCom project and found global loadings ranged between 7 and 30 Tg,
of which
Aerosol optical depth (AOD) correlation coefficients and
biases between 12 km simulations (explicit and parameterised 10:00 UTC) and
MODIS AOD retrievals (
Figure 2 displays the monthly mean (May–September) AODs at 550 nm from the
MODIS Terra satellite with the dust AOD from all the SWAMMA models excluding
dust radiative effects (4E, 12E, 12P and 40P from Table 1). Here the model
AOD at 10:00 UTC has been selected to provide a better time match for the
Terra data which overpass the region at approximately 10:30 LST. As Fig. 2
shows monthly mean values of AOD at approximately 10:00 UTC for both
simulations and satellite retrievals, we believe that this is a good
comparison with which the overall differences in the spatial distribution of
dust in both reality and the simulations can be judged. It is clear that the models are
very similar across all resolutions and all feature a maximum over the
Bodélé depression (
Monthly mean (May–September) maps showing
Monthly mean (May–September) difference maps showing
All the SWAMMA models lack the AOD maximum evident in the MODIS data
from June to August in the central Sahara. We therefore examine factors
affecting the dust emission to see why this might be. Figure 4 shows the
monthly mean (May–September) dust AODs (for all hours), with the corresponding
dust emissions, surface friction velocity over bare soil (
Monthly mean (May–September) aerosol optical depths averaged
over
Figure 6 summarises the seasonal trends of AOD and in factors affecting the
dust AOD in the 12E and 12P models for the northern Sahara (NS), Sahara (SA)
and Sahel (SL) regions. We see that for both simulations the AODs (monthly
mean values of all available times) are poorly simulated with their highest
values in May, whereas MODIS AOD increases from May to a maximum in July
(for NS and SA) and June (for SL). The trend in AOD shown by MODIS
retrievals is consistent with the summertime northwards advance of the
monsoon, rainfall and haboobs (Marsham et al., 2008). MODIS AOD data from
2006–2008 in Fig. 2 of Ridley et al., 2012 indicate that this pattern is
robust and not unique to 2011, indicating that simulations are missing a key
dust-generating mechanism providing a maximum in June–July. Additionally we
see that the explicit convection version generally performs worse than the
parameterised version in this respect. Analysing the contributory factors, the trend
in model AOD follows the trend in dust load, as expected (note that loads
plotted are regional totals scaled by a factor of 5 for NS and SA, and 10
for SL). The dust loads generally follow the trend in dust emissions, except
for May–June in the Sahel where the dust load is boosted by advection from
the Sahara. Dust emission trends are strongly driven by the friction
velocity (
Probability density functions showing the frequency of wind speeds (adjusted to observation height) of different strengths for the observation stations and the closest simulated grid box. Rows indicate the box from Fig. 1 in which the stations are located. Black indicates observations and colours and dashed lines indicate grid spacing and representation of convection of the four simulations.
The explicit treatment of convection is known (from Cascade; Marsham et al., 2011 and Heinold et al., 2013) to have a strong impact on the representation of haboobs in the UM, but here it does not impact the dust fields significantly. We therefore continue our investigation with an evaluation of the near-surface winds (a strong controlling factor in the emission of dust) in both simulations and observations, to further explain why explicitly permitting haboobs has such a small impact on the modelled dust AODs.
Composites around column rainfall exceeding 1 mm h
The hypothesis that explicit convection would produce significant differences
in the dust field for the SWAMMA simulations has been shown to be incorrect.
One potential cause of this is the possibility that the simulated surface
winds do not change very much from one simulation to another. Figure 7 shows
the distribution of wind speeds adjusted to an observation height of 2 m using
the wind profile power law (Touma, 1977; Roberts et al., 2017) at a number of
locations in the Sahel and Sahara for all four simulations (4E, 12E, 12P and
40P) as well as observed winds. There is close agreement in the maximum
frequency of occurrence in the simulations at each of the stations, with the
observations up to 3 m s
Diurnal cycle of dust uplift potential at the five observation stations for all 5 simulated months. Colours and dashed lines are the same as Fig. 7 (black is observations, green is 40 km simulation, red is 12 km simulations and blue is 4 km simulation, dashed lines indicate parameterisation of convection and solid lines indicate explicit convection). Where fewer than 5 days with data were available the diurnal cycle has not been calculated. For clarity the number of days with data has been shown for each panel.
In order to investigate haboob winds, Fig. 8a, b show the anomaly of
10 m wind speed cubed composited around column maximum rainfall rates
greater than 1 mm h
Monthly mean (May–September) diurnal cycles in dust emission
(in
The unchanging overall frequency of different wind speeds (Fig. 7) and the presence of convectively generated cold pools (Fig. 8) can be combined with the findings of Marsham et al. (2013) that up to 50 % of dust emission in the summertime central Saharan hotspot occurs at night due to haboobs. This highlights the need to compare diurnal cycles of the different simulations. Figure 9 shows the diurnal cycle of dust uplift potential (DUP; Marsham et al., 2011) for all simulated months for the five sites for which there are observations. The northern Sahara station, F-138, has a similar development of the diurnal cycle across the 5 simulated months: in both observations and simulations the highest DUP values tend to occur during the day with much lower values at night, and in some months there is a maximum at 09:00 UTC, likely from the breakdown of the nocturnal LLJ. This is as expected given that F-138 is too far north to be strongly or regularly influenced by the cold pools spreading deep into the Sahara. The low night-time values reflect the development of a stable nocturnal boundary layer, which breaks down due to surface heating during daylight hours. F-134 and BBM in the Saharan box show a clearer peak from LLJ breakdown at approximately 09:00 UTC. At F-134 (in both observations and simulations) this process is the dominant feature throughout the entire season. However, further south at BBM, the observations suggest that the morning peak in DUP is similar in magnitude, with an evening peak, in agreement with Marsham et al. (2013). This second peak in DUP associated with haboobs is not well represented in the simulations with the 12 km explicit and 4 km explicit simulations having different diurnal cycles with regard to the evening peak. This is possibly caused by the simulations failing to produce cold pools of sufficient strength as far north as BBM. However, the evening peak at BBM cannot be wholly attributed to cold pools. This is due to the fact that there is a similar, yet smaller, peak present in the simulations, with parameterised convection in June. This is feasibly the impact of the daily night-time surge of the monsoon flow, which is stronger in the parameterised simulations than the explicit simulations (consistent with Birch et al., 2014).
Storm-tracking mesoscale convective system (MCS)
track density for
At the Sahelian stations of Agoufou and Kobou, the diurnal cycle in May
(Fig. 9p, u) is similar to that seen in the Sahara with a morning LLJ
peak in DUP and largely similar diurnal behaviour across all simulations.
However, by June there is evidence of divergent behaviour between the
simulations. At Agoufou and Kobou (Fig. 9q, v) there is an evening peak in
DUP at 16:00–21:00 UTC, which is more pronounced in convection-permitting
simulations. This evening peak grows more pronounced at these stations
from July to August. This evening peak is also particularly noisy: this
behaviour is what would be expected from high DUP values associated with
cold pools due to their production of very high wind values that last on
timescales
Given that it has been shown that convective cold pools are present and are likely to be responsible for a significant modification of the diurnal cycle of winds in the Sahel and as far north as BBM it follows that there should be some modification in the uplift and transport of dust. Figure 10 shows the monthly mean diurnal cycle in dust emissions from the 12 km simulations for the five stations. Although there is some evidence of an evening increase in emissions in the convection-permitting model at BBM in June–August, consistent with the DUP in Fig. 9, this is insufficient to significantly change or improve the dust load and AOD. Dust emissions at stations in the Sahel (Agoufou and Kobou) are reduced in the explicit version: this is likely to be due to the increased soil moisture in that region (as demonstrated in Fig. 6). In addition to such limits imposed by the surface characteristics on the uplift of dust in the model, it is also possible that there is some behaviour of convective storms and their associated cold pools that means that they do not lift dust; for example the wrong size, lifetime or location. This is examined in the next section.
Case study of a large cold-pool event that occurred on
the morning of 23 August 2011 that is present both
in the observations and in the 4 km explicit simulation. Panels
To investigate the nature of the storms that are responsible for the
generation of cold pools, a storm-tracking approach has been used. This takes
advantage of the availability of satellite observations of outgoing long-wave
radiation from which the brightness temperature can be easily derived, and
tracking is performed on features with a brightness temperature below
Figure 11e shows the distributions of the MCS duration (to the nearest hour),
highlighting the fact that the 4 and 12 km simulations have MCSs that last
longer on average than those in SEVIRI; it is only storms that live beyond
30 h for the 4 km simulations and 47 h for the 12 km simulations
that the frequency of occurrence first drops below the values seen from
observations. This abundance of events (even MCSs that are smaller than
those observed) and the fact that convective cold pools are clearly being
produced in the simulations (despite their reduced strength) suggests that
the lack of emission in the simulation south of 17
In interpreting the storm-track-based analysis discussed above, it is
useful to examine sample images of observed and modelled large storms.
Figure 12 is a case study of a large cold-pool event that occurred on
23 August 2011. It was well represented in the 4 km simulation in
that the timing and location of initiation of the system was roughly
correct, after which a large MCS developed and produced a cold pool which
spread north and west into the Sahara. Although we do not necessarily expect
an accurate one-to-one correspondence between observed and modelled storms
this far into the simulation, the case shown does share key similarities and
is one of the larger modelled storms from the simulated period. The cold
pool in the simulation can be seen through both the elevated friction
velocity over bare soil as well as the spreading of air away from the MCS
shown in the 10 m wind vectors. Similarly, the cold pool generated in
reality can be identified through the occurrence of arc clouds along the
leading edge of the cold pool and the magenta colour that identifies raised
dust within the cold pool in the SEVIRI RGB false colour dust images. The
impact that this cold pool has on dust is assessed through the daytime
averages of the dust AOD from the 4 km simulation and the SEVIRI AERUS-GEO
AOD product. There is clearly a strong AOD signal associated with the cold
pools in both measures. However, the signal in the simulation is dwarfed by
the high levels over the western part of the domain (at least partially
associated with erroneously high uplift over the Western Sahara).
In the SEVIRI AERUS-GEO product the AOD feature in the central
Sahara is comparable in magnitude to the transported plume over the Atlantic
and is much more clearly linked to uplift caused by strong near-surface
winds associated with the passage of a convective cold pool. This is
consistent with the maximum mean hourly observed wind on this day at BBM
being 11.4 m s
We have investigated whether biases in dust AOD over the Sahara and Sahel, known to exist in many global and regional models, can be improved in the Met Office Unified Model (UM) by using an explicit rather than parameterised formulation of convection. It was hypothesised that explicit resolution of the strong winds associated with cold-pool outflows which generate dust storms (haboobs) in summertime West Africa might enhance the AOD in the central Saharan heat low (SHL) region, where haboobs have been observed to be a key uplift mechanism and where a dust maximum is present in satellite retrievals but missing in many models. Regional versions of the UM with prognostic dust at 4, 12 and 40 km grid spacings were used, with explicit convection at 4 and 12 km and parameterised convection at 12 and 40 km. These SWAMMA simulations enable a clean comparison between models at 12 km resolution with explicit and parameterised convection (differing only in representation of convection). This provides a seamless approach, with the model configurations ranging from high-resolution (4 km) convection-permitting to a configuration similar to a climate model. In this respect a potentially valuable property of the SWAMMA simulations is their similarity with CMIP5 simulations in behaviour and AOD features, indicating that investigation of process errors in SWAMMA are likely to identify and provide knowledge about similar errors in the CMIP5 data set.
The results show that all SWAMMA simulations have very similar dust AOD
fields, despite explicit convection significantly changing the wind fields
and overall clearly demonstrate how improving the representation of cold
pools, known to be critical to dust uplift, is a necessary but not
sufficient condition for improving AOD fields. When convection is modelled
explicitly, cold pools (haboobs) are present and the diurnal cycle in surface
winds is better represented. However, in the southern Sahara the rare very
strong wind events that result from haboobs and cause the most intense dust
storms are still absent in all simulations. The analysis of composite cold pools
and storm tracking shows that, although storms exist far enough north
in convection-permitting simulations, the storms are not sufficiently large,
which is likely to limit both the intensity of the cold-pool winds and the
northwards propagation of the resultant cold pools into the southern Sahara,
and so it is consistent with the weaker than observed winds in that key region.
This interpretation is supported by a simple representative case study of a
large storm that shows how in the model, even when a large system is
generated it does not raise quantities of dust comparable to those seen in
satellite retrievals. Consistent with past studies of long-duration
large-domain runs, in the explicit runs there is a reduction in the strength
of the morning low-level jet (LLJ), which compensates for the haboob uplift.
This means that the increase in dust emissions achieved by the strengthened
evening (haboob) winds does not produce any overall increase in the AOD in
the SHL region, since the LLJ winds are reduced. The results here likely
contrast with those of Chaboureau et al. (2016), where explicit haboobs did
improve dust fields for several reasons: (i) in the Chaboureau set-up it is
not expected that the explicit convection weakens the low-level jet because
their simulations are initialised daily and run for between 24 and 72 h
depending on the model (as seen in comparisons between 2-day and 10-day runs in
Marsham et al., 2011), (ii) the Chaboureau models have a different land
surface to the UM and different dust emission schemes which are
individually tuned so that AOD changes cannot be attributed solely to the
choice of convection scheme and (iii) the Chaboureau models with explicit
convection have a more limited southern boundary than the SWAMMA simulation so
their results are more focussed on Saharan rather than on Sahelian dust
(results are shown for the region 13–31
The results here also suggest several key problems with the modelled land
surface in the UM. The models have almost no dust uplift in the Sahel,
whereas in reality convective storms over the Sahel do raise dust (Flamant
et al., 2007; Marsham et al., 2009; Roberts and Knippertz, 2014). South of 15
The issues discussed above provide a stark demonstration of the number of marginal processes that must be well simulated in any model to capture the seasonal evolution of the dust field over Africa. Models must capture the seasonal evolution of the continental-scale thermodynamics gradients, which is itself non-trivial and dependent on convection (Marsham et al., 2013); the location of the moist convection, particularly the marginal convection close to both the leading edge of the monsoon and close to the sharp gradient in soil moisture and vegetation present from the Sahel to the Sahara; the tail of strong winds from cold pools and the low-level jet breakdown; the time evolution of skin soil moisture and vegetation (and therefore roughness); and the soil properties themselves. Given these challenges it is perhaps not surprising that Evan et al. (2014) conclude that the CMIP models are unable to capture any of the salient features of northern African dust emission and transport. An improved representation of cold pools in dust models is clearly necessary but not in itself sufficient for improving AOD fields within the UM. Future evaluations of dust models should ensure that winds as well as dust are evaluated to ensure that models are getting the right answers for the right reasons (noting the value of observed not analysed winds due to the large biases in analyses). Although parameterisations of haboobs (e.g. Pantillon, 2015, 2016) are clearly valuable, corresponding improvements are also needed in soil moisture, vegetation and soil properties in models. There is a need for potential scale dependences for maximum wind speeds in convection-permitting models to be investigated. It is also clear that winds from explicit models (the UM and potentially other models) may still have significant biases, even though haboobs are represented. Therefore estimates of the fraction of dust uplift from haboobs from such models (e.g. Heinold et al., 2013), although very valuable, may be a significant underestimate and must be treated with caution.
As yet the SWAMMA and Fennec data have not been moved into long-term storage. However data are available on request. For more information please contact Alexander J. Roberts via the author correspondence address.
The authors declare that they have no conflict of interest.
We would first like to thank the anonymous reviewers and the co-editor,
Yves Balkanski, for their valuable insight and help in improving this paper.
The SWAMMA project was funded by the UK Natural Environmental Research
Council (NERC) standard grant NE/L005352/1. John Marsham was also funded by
AMMA 2050 (NE/M020126/1), IMPALA (NE/M017176/1) and DACCIWA (FP7/2007-2013
under grant agreement no. 603502). This work used the ARCHER UK National
Supercomputing Service (